Create picking.py

tools/picking.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from ortools.constraint_solver import routing_enums_pb2
+from ortools.constraint_solver import pywrapcp
+
+
+def optimize_picking_route(df: pd.DataFrame):
+    df = df.copy()
+
+    # Convert to points
+    coords = df[["Aisle", "Rack"]].values.tolist()
+
+    # Distance matrix (Manhattan)
+    size = len(coords)
+    dist = [[abs(a1 - a2) + abs(r1 - r2)
+             for (a2, r2) in coords] for (a1, r1) in coords]
+
+    # Solver
+    manager = pywrapcp.RoutingIndexManager(size, 1, 0)
+    routing = pywrapcp.RoutingModel(manager)
+
+    def dist_callback(i, j):
+        return dist[manager.IndexToNode(i)][manager.IndexToNode(j)]
+
+    transit_idx = routing.RegisterTransitCallback(dist_callback)
+    routing.SetArcCostEvaluatorOfAllVehicles(transit_idx)
+
+    params = pywrapcp.DefaultRoutingSearchParameters()
+    params.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
+
+    solution = routing.SolveWithParameters(params)
+
+    order = []
+    idx = routing.Start(0)
+    while not routing.IsEnd(idx):
+        order.append(manager.IndexToNode(idx))
+        idx = solution.Value(routing.NextVar(idx))
+
+    ordered_df = df.iloc[order]
+
+    # Plot
+    plt.figure(figsize=(5, 5))
+    plt.plot(ordered_df["Aisle"], ordered_df["Rack"], marker="o")
+    plt.title("Optimized Picking Route")
+    plt.xlabel("Aisle")
+    plt.ylabel("Rack")
+    img_path = "/tmp/route_plot.png"
+    plt.savefig(img_path)
+    plt.close()
+
+    return img_path, ordered_df